Machine Learning: A Visual Starter Course

This starter course is for anyone who is curious about machine learning but doesn’t know where to begin. If you've found yourself in other introductory machine learning courses and started to feel lost halfway through, this is the book for you. If you don't understand various terms like vectors, hyperplanes, and sigmoid functions, then this is also the book for you.


This starter course is not a picture story book. But does include lots of visual examples that break down algorithms into a digestible and practical format.


Please note that you also won’t come out of the book a machine learning engineer. As a starter course, this book connects the dots and offers the crash course I wish I had when I first started. The kind of guide I wish had before I started taking on introductory courses that presume you’re two days out from an advanced mathematics exam.


That’s why this introductory course doesn’t go further on the subject than other introductory books, but rather, goes a step back. A half-step backward in order to help everyone make his or her first stride in machine learning.


Topics covered:

- Downloading free datasets
- Machine learning kit box
- Data scrubbing techniques, including one-hot encoding and binning
- Regression
- Clustering, including k-means and k-nearest neighbors
- Descending dimension algorithms
- Anomaly detection and SVM
- The basics of neural networks